Understanding mobile service diffusion as an evolutionary process: A study of the Swedish market

Abstract: This thesis aims to highlight the connections between the diffusion of innovation theory and the evolutionary models for technological changes within the context of mobile communication research. On the basis of empirical findings, the discussion focuses on addressing three research questions: Why should mobile service diffusion be understood as an evolutionary process? How should mobile service diffusion be explained and modelled using evolutionary conceptions? And in what way the evolutionary framework could influence future mobile service diffusion studies? Based on empirical observations and a literature study, this thesis argues that mobile service diffusion involves dynamic, developmental and historical economic process which is comparable to an evolutionary process. Some essential features of evolutionary processes can also be observed empirically along the mobile service diffusion. For instance, the presence of various generations of mobile service technology along the diffusion timeline as well as different intensities of mobile service use, i.e. single subscriptions and multiple subscriptions, indicates that the variation characterizes the diffusion process of mobile service. The cord-cutter population implicitly indicates the presence of selection mechanism of individuals who choose to retain mobile-only communications rather than other type of communication. Similarly the existence of mobile service non-users also implicitly indicates that retention exists along the diffusion process. All these indications suggest that the evolutionary concepts are relevant in order to comprehend mobile service diffusion. To explain and model mobile service diffusion using an evolutionary framework, this thesis underlines the importance of data granularity and the use of a relevant diffusion model. The use of data granularity is critical to represent the variation and to serve as a proxy for making trend projection based on the level of interest. The use of a relevant diffusion model is essential to describe the pattern of the data according to selection mechanisms that determine the diffusion process. The evolutionary variation and selection mechanisms are also considered in two examples of diffusion modelling that address the level of mobile service use and intergenerational technology effects. The results show intuitive trend projections as well as realistic understanding toward the process of mobile service diffusion which are helpful for business strategy and policy planning. However the proposed approach is still unable to address different actors and forces that may internally or externally influence the evolutionary process of mobile service diffusion (i.e. dynamics in pricing, inter-technological substitutions and complementarities, service bundling, etc.). This suggests that future studies in mobile service diffusion should take into account the evolutionary conceptions that could model dynamic interactions of relevant actors and interests in mobile service ecosystem.